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## chr (16): Office, State, Area, RepCandidate, RepStatus, DemCandidate, DemSta...
## dbl (14): raceYear, RepVotes, DemVotes, ThirdVotes, OtherVotes, PluralityVot...
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## chr (16): Office, State, Area, RepCandidate, RepStatus, DemCandidate, DemSta...
## dbl (14): raceYear, RepVotes, DemVotes, ThirdVotes, OtherVotes, PluralityVot...
## lgl (1): CensusPop
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## 1 1 1Note that 2018 in comparison to the model in lab that included more years has a greater correlation it seems of voter turnout and democratic major vote pct.
##
## Call:
## lm(formula = DemVotesMajorPercent ~ turnout, data = dist_pv_cvap_closed)
##
## Residuals:
## Min 1Q Median 3Q Max
## -34.060 -12.411 -2.361 11.883 45.749
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 46.623 5.607 8.314 1.52e-15 ***
## turnout 11.132 11.075 1.005 0.315
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 16.4 on 393 degrees of freedom
## Multiple R-squared: 0.002564, Adjusted R-squared: 2.583e-05
## F-statistic: 1.01 on 1 and 393 DF, p-value: 0.3155
##
## Call:
## glm(formula = DemVotesMajorPct ~ turnout, family = binomial(link = "logit"),
## data = dist_pv_cvap_closed)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -0.70849 -0.24881 -0.04725 0.23968 1.02030
##
## Coefficients:
## Estimate Std. Error z value Pr(>|z|)
## (Intercept) -0.1355 0.6847 -0.198 0.843
## turnout 0.4462 1.3528 0.330 0.741
##
## (Dispersion parameter for binomial family taken to be 1)
##
## Null deviance: 44.956 on 394 degrees of freedom
## Residual deviance: 44.847 on 393 degrees of freedom
## AIC: 551.77
##
## Number of Fisher Scoring iterations: 3Adding voter turnout into my predictive model to see how it might affect my results.
| Observations | 1308 |
| Dependent variable | turnout |
| Type | OLS linear regression |
| F(53,1254) | 51.97 |
| R² | 0.69 |
| Adj. R² | 0.67 |
| Est. | S.E. | t val. | p | |
|---|---|---|---|---|
| (Intercept) | 183.77 | 5.01 | 36.69 | 0.00 |
| year | -0.09 | 0.00 | -36.70 | 0.00 |
| state.xAlaska | 0.08 | 0.05 | 1.82 | 0.07 |
| state.xArizona | -0.04 | 0.02 | -1.70 | 0.09 |
| state.xArkansas | -0.05 | 0.03 | -1.99 | 0.05 |
| state.xCalifornia | 0.01 | 0.02 | 0.57 | 0.57 |
| state.xColorado | 0.09 | 0.02 | 4.00 | 0.00 |
| state.xConnecticut | 0.08 | 0.02 | 3.26 | 0.00 |
| state.xDelaware | -0.13 | 0.05 | -2.75 | 0.01 |
| state.xFlorida | -0.02 | 0.02 | -0.84 | 0.40 |
| state.xGeorgia | 0.03 | 0.02 | 1.53 | 0.13 |
| state.xHawaii | -0.03 | 0.03 | -0.94 | 0.34 |
| state.xIdaho | -0.03 | 0.03 | -0.87 | 0.39 |
| state.xIllinois | 0.07 | 0.02 | 3.95 | 0.00 |
| state.xIndiana | -0.02 | 0.02 | -1.16 | 0.25 |
| state.xIowa | 0.09 | 0.03 | 3.30 | 0.00 |
| state.xKansas | 0.04 | 0.03 | 1.32 | 0.19 |
| state.xKentucky | 0.02 | 0.02 | 0.70 | 0.48 |
| state.xLouisiana | -0.13 | 0.02 | -5.48 | 0.00 |
| state.xMaine | 0.13 | 0.03 | 4.10 | 0.00 |
| state.xMaryland | 0.07 | 0.02 | 3.35 | 0.00 |
| state.xMassachusetts | 0.02 | 0.02 | 1.16 | 0.25 |
| state.xMichigan | 0.08 | 0.02 | 4.01 | 0.00 |
| state.xMinnesota | 0.19 | 0.02 | 8.69 | 0.00 |
| state.xMississippi | -0.07 | 0.03 | -2.59 | 0.01 |
| state.xMissouri | -0.01 | 0.02 | -0.26 | 0.79 |
| state.xMontana | -0.14 | 0.05 | -2.85 | 0.00 |
| state.xNebraska | 0.13 | 0.03 | 4.51 | 0.00 |
| state.xNevada | -0.00 | 0.03 | -0.07 | 0.95 |
| state.xNew Hampshire | 0.11 | 0.03 | 3.28 | 0.00 |
| state.xNew Jersey | 0.03 | 0.02 | 1.37 | 0.17 |
| state.xNew Mexico | 0.04 | 0.03 | 1.34 | 0.18 |
| state.xNew York | -0.03 | 0.02 | -1.40 | 0.16 |
| state.xNorth Carolina | 0.02 | 0.02 | 1.08 | 0.28 |
| state.xNorth Dakota | 0.04 | 0.05 | 0.90 | 0.37 |
| state.xOhio | 0.03 | 0.02 | 1.30 | 0.19 |
| state.xOklahoma | -0.10 | 0.02 | -3.87 | 0.00 |
| state.xOregon | 0.04 | 0.03 | 1.65 | 0.10 |
| state.xPennsylvania | 0.03 | 0.02 | 1.73 | 0.08 |
| state.xRhode Island | 0.15 | 0.03 | 4.44 | 0.00 |
| state.xSouth Carolina | -0.02 | 0.02 | -0.88 | 0.38 |
| state.xSouth Dakota | -0.02 | 0.05 | -0.39 | 0.70 |
| state.xTennessee | -0.07 | 0.02 | -3.23 | 0.00 |
| state.xTexas | -0.04 | 0.02 | -2.03 | 0.04 |
| state.xUtah | 0.02 | 0.03 | 0.89 | 0.37 |
| state.xVermont | 0.09 | 0.05 | 2.02 | 0.04 |
| state.xVirginia | 0.04 | 0.02 | 2.05 | 0.04 |
| state.xWashington | 0.09 | 0.02 | 4.16 | 0.00 |
| state.xWest Virginia | -0.01 | 0.03 | -0.17 | 0.86 |
| state.xWisconsin | 0.15 | 0.02 | 7.03 | 0.00 |
| state.xWyoming | 0.10 | 0.05 | 2.14 | 0.03 |
| president_partyR | 0.52 | 0.01 | 39.68 | 0.00 |
| cvap | 0.00 | 0.00 | 15.55 | 0.00 |
| winner_candidate_incIncumbent | -0.01 | 0.01 | -2.16 | 0.03 |
| Standard errors: OLS |
Plotting differences in margin for turnout to test the accuracy of my prediction variable for 2022 turnout to add to my model later. Red indicates that the actual value is less than predicted value, therefore my model is under predicting in the red areas. The same goes for the blue. Where its more blue, such as in Florida, the predictive model is having a hard time and is over predicting these areas for voter turnout.